In this scenario, we consider a patient Bob who is 34 years old and lives alone at his home and has been diagnosed with Obstructive Sleep Apnea (OSA) disease. His assigned Medical Professional monitors him through RPM, shown in Fig. REF . His doctor prescribes medicine or treatment through the RPM ecosystem. In our work, we used this anomaly detection model [1]} to identify the misbehaving nodes in the RPM ecosystem using medical and smart home devices. For instance, a smart blood pressure machine reports hypertension of 150. However, other sensor nodes, such as the motion sensor, describe that the patient is moving normally in his living room. It can be easily identified that the smart blood pressure machine reports the wrong data of the patient. If any anomaly is identified using this anomaly detection model in the RPM ecosystem, that misbehaving report is sent to the RA. We provide Algorithm REF , showing the steps to identify the misbehaving sensor nodes for this model.
